Abstract
Dynamic characterization of the lesion enhancement pattern can improve the classification performance of small diagnostically challenging lesions on dynamic-contrast enhanced MRI. This involves extraction of texture features from all post-contrast images of the lesion rather than using the first post-contrast image alone. In this study, statistical texture features derived from gray-level co-occurrence matrices are extracted from all five post-contrast images of 60 lesions and then used in a supervised learning task with a support vector regressor. Our results show that this approach significantly improves the performance of classifying small lesions (p < 0.05). This suggests that such dynamic characterization of lesion enhancement has significant potential in assisting breast cancer diagnosis for small lesions.
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References
Fischer, D., Wurdinger, S., Boettcher, J., Malich, A., Kaiser, W.: Further signs in the evaluation of magnetic resonance mammography: a retrospective study. Investigative Radiology 40(7), 430–435 (2005)
Schlossbauer, T., Leinsinger, G., Wismüller, A., Lange, O., Scherr, M., Meyer-Baese, A., Reiser, M.: Classification of small contrast enhancing breast lesions in dynamic magnetic resonance imaging using a combination of morphological criteria and dynamic analysis based on unsupervised vector-quantization. Investigative Radiology 43(1), 56–64 (2008)
Leinsinger, G., Schlossbauer, T., Scherr, M., Lange, O., Reiser, M., Wismüller, A.: Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions? European Radiology 16(5), 1138–1146 (2006)
Lerski, R.A., Straughan, K., Schad, L.R., Boyce, D., Blüml, S., Zuna, I.: Tissue characterization by magnetic-resonance spectroscopy and imaging - results of a concerted research-project of the european-economic-community.8. MR image texture analysis - an approach to tissue characterization. Magnetic Resonance Imaging 11(6), 873–887 (1993)
Haralick, R.M., Shanmuga, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems Man and Cybernetics Smc3(6), 610–621 (1973)
Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979)
Korfiatis, P., Kalogeropoulou, C., Karahaliou, A., Kazantzi, A., Skiadopoulos, S., Costaridou, L.: Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Medical Physics 35(12), 5290–5302 (2008)
Chan, H.P., Wei, D.T., Helvie, M.A., Sahiner, B., Adler, D.D., Goodsitt, M.M., Petrick, N.: Computer-aided classification of mammographic masses and normal tissue - linear discriminant-analysis in texture feature space. Physics in Medicine and Biology 40(5), 857–876 (1995)
Wilhjelm, J.E., Gronholdt, M.L.M., Wiebe, B., Jespersen, S.K., Hansen, L.K., Sillesen, H.: Quantitative analysis of ultrasound B-mode images of carotid atherosclerotic plaque: Correlation with visual classification and histological examination. IEEE Transactions on Medical Imaging 17(6), 910–922 (1998)
Gibbs, P., Turnbull, L.W.: Textural analysis of contrast-enhanced MR images of the breast. Magnetic Resonance in Medicine 50(1), 92–98 (2003)
Chen, W., Giger, M.L., Li, H., Bick, U., Newstead, G.M.: Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magnetic Resonance in Medicine 58(3), 562–571 (2007)
Nie, K., Chen, J.H., Yu, H.J., Chu, Y., Nalcioglu, O., Su, M.Y.: Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Academic Radiology 15(12), 1513–1525 (2008)
Drucker, H., Burges, C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, vol. 9, pp. 155–161 (1996)
Chen, W.J., Giger, M.L., Bick, U.: A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Academic Radiology 13(1), 63–72 (2006)
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Nagarajan, M.B., Huber, M.B., Schlossbauer, T., Leinsinger, G., Krol, A., Wismüller, A. (2011). Classifying Small Lesions on Breast MRI through Dynamic Enhancement Pattern Characterization. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_43
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DOI: https://doi.org/10.1007/978-3-642-24319-6_43
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